May 20, 2021
May 20, 2021
Contributor: Laurence Goasduff
Data and analytics leaders who share data externally generate three times more measurable economic benefit than those who do not.
Managing data and creating insights is not enough to accelerate digital business transformation. These activities must deliver measurable business outcomes. According to the Sixth Annual Gartner Chief Data Officer Survey, respondents who successfully increased data sharing led D&A teams that were 1.7 times more effective at showing demonstrable, verifiable value to D&A stakeholders.
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“Data sharing is the way to optimize higher-relevant data, generating more robust data and analytics to solve business challenges and meet enterprise goals,” says Lydia Clougherty Jones, Senior Director Analyst, Gartner. “D&A leaders who promote data sharing have more stakeholder engagement and influence than those who do not.”
Survey respondents reported that data sharing is a business-facing key performance indicator of achieving effective stakeholder engagement and providing enterprise value.
The survey also showed that promoting data sharing and breaking down data silos was most often linked to high-performing D&A teams providing value to the organization.
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Gartner predicts that by 2023, organizations that promote data sharing will outperform their peers on most business value metrics. Yet, at the same time, Gartner predicts that through 2022, less than 5% of data-sharing programs will correctly identify trusted data and locate trusted data sources.
“There should be more collaborative data sharing unless there is a vetted reason not to, as not sharing data frequently can hamper business outcomes and be detrimental,” says Clougherty Jones.
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Many organizations inhibit access to data, preserve data silos and discourage data sharing. This undermines the efforts to maximize business and social value from data and analytics — at a time when COVID-19 is driving demand for data and analytics to unprecedented levels.
The traditional “don’t share data unless” mindset should be replaced with “must share data unless.” By recasting data sharing as a business necessity, data and analytics leaders will have access to the right data at the right time, enabling more robust data and analytics strategies that deliver business benefit and achieve digital transformation.
Although it’s not easy to change the status quo, data and analytics leaders must focus on establishing trust-based mechanisms and preparing a data-sharing environment.
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If you do not introduce trust throughout your data-sharing process, you cannot achieve business value from the data you collect. Gartner predicts that through 2023, organizations that can instill digital trust will be able to participate in 50% more ecosystems, expanding revenue-generation opportunities.
Develop trust-based mechanisms that establish high levels of trust in the data source and separately in the trustworthiness of the data. This allows you to align appropriate data use with your business goals, both within and outside your organization.
It’s important to trust the quality of the data you collect, use and share to match your business context and requirements. Separately, organizations must trust their data sources so that they can rely on (and pass on to others) appropriate and enforceable rights to use, reuse, share and reshare data.
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Adopt digital trust technologies, such as blockchain smart contracts, to securely collect data and efficiently transfer and share assets of monetary or nonmonetary value.
Overall, use data-quality metrics and augmented data catalogs to compile your data and the evaluations done to determine the trustworthiness of data sources. By 2021, organizations that offer users access to a curated catalog of internally and externally prepared data will realize 100% more business value from analytics investments than those that do not.
To establish both an IT and people culture that fosters data sharing, work with your business leaders across business units to create a data-sharing mindset. Foster collaboration even if the purposes of data sharing may differ or conflict, eschewing a data “ownership” culture — by identifying the emotional impacts and inherent biases that hamper data sharing.
Within your IT department, distinguish your data management strategy between data warehouses, data lakes and data hubs. Create new and flexible data management practices that adapt to uncertain and changing environments.
Prioritize use cases in which increased data sharing will yield maximum alignment with business outcomes — including increased costs savings, net new revenue or nonmonetary value creation, and improved risk mitigation decision making.
This article has been updated (originally published in October, 2020) to reflect new events, conditions or research.
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